Overview

Dataset statistics

Number of variables20
Number of observations1190
Missing cells2341
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory169.8 KiB
Average record size in memory146.1 B

Variable types

Numeric12
Categorical5
Unsupported1
Boolean2

Alerts

name has a high cardinality: 1190 distinct values High cardinality
url has a high cardinality: 1190 distinct values High cardinality
first_series_time has a high cardinality: 121 distinct values High cardinality
peak_time has a high cardinality: 130 distinct values High cardinality
tournaments_list has a high cardinality: 697 distinct values High cardinality
id is highly correlated with elo and 5 other fieldsHigh correlation
elo is highly correlated with id and 7 other fieldsHigh correlation
rank is highly correlated with elo and 7 other fieldsHigh correlation
voobly_id is highly correlated with id and 2 other fieldsHigh correlation
first_series_timestamp is highly correlated with id and 4 other fieldsHigh correlation
peak_timestamp is highly correlated with id and 4 other fieldsHigh correlation
peak_elo is highly correlated with elo and 6 other fieldsHigh correlation
series_played is highly correlated with elo and 5 other fieldsHigh correlation
series_won is highly correlated with elo and 5 other fieldsHigh correlation
games_played is highly correlated with elo and 5 other fieldsHigh correlation
tournaments_played is highly correlated with elo and 6 other fieldsHigh correlation
inactive is highly correlated with id and 7 other fieldsHigh correlation
retired is highly correlated with id and 4 other fieldsHigh correlation
voobly_id has 1095 (92.0%) missing values Missing
steam_id has 1190 (100.0%) missing values Missing
first_series_timestamp has 14 (1.2%) missing values Missing
first_series_time has 14 (1.2%) missing values Missing
peak_timestamp has 14 (1.2%) missing values Missing
peak_time has 14 (1.2%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
name is uniformly distributed Uniform
url is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
id has unique values Unique
name has unique values Unique
url has unique values Unique
steam_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
series_played has 14 (1.2%) zeros Zeros
series_won has 594 (49.9%) zeros Zeros
games_played has 19 (1.6%) zeros Zeros
tournaments_played has 14 (1.2%) zeros Zeros

Reproduction

Analysis started2022-12-06 21:53:19.555496
Analysis finished2022-12-06 21:54:13.725344
Duration54.17 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct1190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean594.5
Minimum0
Maximum1189
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:13.930348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59.45
Q1297.25
median594.5
Q3891.75
95-th percentile1129.55
Maximum1189
Range1189
Interquartile range (IQR)594.5

Descriptive statistics

Standard deviation343.6677174
Coefficient of variation (CV)0.5780785827
Kurtosis-1.2
Mean594.5
Median Absolute Deviation (MAD)297.5
Skewness0
Sum707455
Variance118107.5
MonotonicityStrictly increasing
2022-12-06T15:54:14.295374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
7991
 
0.1%
7971
 
0.1%
7961
 
0.1%
7951
 
0.1%
7941
 
0.1%
7931
 
0.1%
7921
 
0.1%
7911
 
0.1%
7901
 
0.1%
Other values (1180)1180
99.2%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
11891
0.1%
11881
0.1%
11871
0.1%
11861
0.1%
11851
0.1%
11841
0.1%
11831
0.1%
11821
0.1%
11811
0.1%
11801
0.1%

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean661.502521
Minimum1
Maximum1294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:14.728408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile74.45
Q1348.25
median667.5
Q3975.75
95-th percentile1231.55
Maximum1294
Range1293
Interquartile range (IQR)627.5

Descriptive statistics

Standard deviation368.6347662
Coefficient of variation (CV)0.5572688757
Kurtosis-1.166411888
Mean661.502521
Median Absolute Deviation (MAD)313.5
Skewness-0.03979564987
Sum787188
Variance135891.5908
MonotonicityNot monotonic
2022-12-06T15:54:15.111435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7671
 
0.1%
2531
 
0.1%
10911
 
0.1%
12681
 
0.1%
12241
 
0.1%
12001
 
0.1%
6861
 
0.1%
4821
 
0.1%
1261
 
0.1%
3381
 
0.1%
Other values (1180)1180
99.2%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
12941
0.1%
12931
0.1%
12921
0.1%
12911
0.1%
12901
0.1%
12891
0.1%
12881
0.1%
12871
0.1%
12861
0.1%
12851
0.1%

name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
000189AKM
 
1
psNps
 
1
Project Exile
 
1
Professor4k
 
1
prisma09
 
1
Other values (1185)
1185 

Length

Max length19
Median length15
Mean length7.307563025
Min length2

Characters and Unicode

Total characters8696
Distinct characters95
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1190 ?
Unique (%)100.0%

Sample

1st row000189AKM
2nd row000201AKM
3rd row000934AKM
4th row2scareD
5th row31_

Common Values

ValueCountFrequency (%)
000189AKM1
 
0.1%
psNps1
 
0.1%
Project Exile1
 
0.1%
Professor4k1
 
0.1%
prisma091
 
0.1%
pren1
 
0.1%
Prelude1
 
0.1%
Pray_4yourlife1
 
0.1%
PraYs1
 
0.1%
pRaXiS1
 
0.1%
Other values (1180)1180
99.2%

Length

2022-12-06T15:54:15.493467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the4
 
0.3%
rex2
 
0.2%
wrath2
 
0.2%
tobi2
 
0.2%
of2
 
0.2%
mr2
 
0.2%
chris2
 
0.2%
nick2
 
0.2%
escape2
 
0.2%
lion2
 
0.2%
Other values (1215)1215
98.2%

Most occurring characters

ValueCountFrequency (%)
a723
 
8.3%
e690
 
7.9%
o566
 
6.5%
i553
 
6.4%
r462
 
5.3%
n436
 
5.0%
l346
 
4.0%
s296
 
3.4%
t289
 
3.3%
_274
 
3.2%
Other values (85)4061
46.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6368
73.2%
Uppercase Letter1753
 
20.2%
Connector Punctuation274
 
3.2%
Decimal Number209
 
2.4%
Space Separator50
 
0.6%
Other Letter24
 
0.3%
Other Punctuation7
 
0.1%
Open Punctuation5
 
0.1%
Close Punctuation5
 
0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a723
11.4%
e690
 
10.8%
o566
 
8.9%
i553
 
8.7%
r462
 
7.3%
n436
 
6.8%
l346
 
5.4%
s296
 
4.6%
t289
 
4.5%
u233
 
3.7%
Other values (21)1774
27.9%
Uppercase Letter
ValueCountFrequency (%)
S155
 
8.8%
M121
 
6.9%
T117
 
6.7%
D107
 
6.1%
A107
 
6.1%
R105
 
6.0%
C104
 
5.9%
N90
 
5.1%
B84
 
4.8%
K76
 
4.3%
Other values (16)687
39.2%
Other Letter
ValueCountFrequency (%)
3
 
12.5%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (11)11
45.8%
Decimal Number
ValueCountFrequency (%)
051
24.4%
233
15.8%
133
15.8%
323
11.0%
421
10.0%
513
 
6.2%
912
 
5.7%
811
 
5.3%
67
 
3.3%
75
 
2.4%
Other Punctuation
ValueCountFrequency (%)
.6
85.7%
:1
 
14.3%
Connector Punctuation
ValueCountFrequency (%)
_274
100.0%
Space Separator
ValueCountFrequency (%)
50
100.0%
Open Punctuation
ValueCountFrequency (%)
[5
100.0%
Close Punctuation
ValueCountFrequency (%)
]5
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8121
93.4%
Common551
 
6.3%
Han24
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a723
 
8.9%
e690
 
8.5%
o566
 
7.0%
i553
 
6.8%
r462
 
5.7%
n436
 
5.4%
l346
 
4.3%
s296
 
3.6%
t289
 
3.6%
u233
 
2.9%
Other values (47)3527
43.4%
Han
ValueCountFrequency (%)
3
 
12.5%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (11)11
45.8%
Common
ValueCountFrequency (%)
_274
49.7%
051
 
9.3%
50
 
9.1%
233
 
6.0%
133
 
6.0%
323
 
4.2%
421
 
3.8%
513
 
2.4%
912
 
2.2%
811
 
2.0%
Other values (7)30
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII8663
99.6%
CJK24
 
0.3%
None9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a723
 
8.3%
e690
 
8.0%
o566
 
6.5%
i553
 
6.4%
r462
 
5.3%
n436
 
5.0%
l346
 
4.0%
s296
 
3.4%
t289
 
3.3%
_274
 
3.2%
Other values (59)4028
46.5%
None
ValueCountFrequency (%)
é3
33.3%
ä3
33.3%
ì1
 
11.1%
ï1
 
11.1%
ô1
 
11.1%
CJK
ValueCountFrequency (%)
3
 
12.5%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (11)11
45.8%

elo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct221
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1930.588235
Minimum1853
Maximum2357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:15.841033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1853
5-th percentile1884
Q11894
median1898
Q31956.75
95-th percentile2050.55
Maximum2357
Range504
Interquartile range (IQR)62.75

Descriptive statistics

Standard deviation69.67952734
Coefficient of variation (CV)0.03609238162
Kurtosis9.949129223
Mean1930.588235
Median Absolute Deviation (MAD)7
Skewness2.791765093
Sum2297400
Variance4855.236531
MonotonicityNot monotonic
2022-12-06T15:54:16.141858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1896150
 
12.6%
189491
 
7.6%
189770
 
5.9%
189861
 
5.1%
190041
 
3.4%
189136
 
3.0%
189234
 
2.9%
188829
 
2.4%
190127
 
2.3%
189926
 
2.2%
Other values (211)625
52.5%
ValueCountFrequency (%)
18531
0.1%
18581
0.1%
18601
0.1%
18631
0.1%
18661
0.1%
18672
0.2%
18681
0.1%
18701
0.1%
18711
0.1%
18722
0.2%
ValueCountFrequency (%)
23571
0.1%
23451
0.1%
23361
0.1%
23262
0.2%
23231
0.1%
23181
0.1%
22891
0.1%
22851
0.1%
22661
0.1%
22611
0.1%

rank
Real number (ℝ≥0)

HIGH CORRELATION

Distinct237
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean824.3798319
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:16.456901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60.45
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation352.5418362
Coefficient of variation (CV)0.4276449066
Kurtosis0.4269363566
Mean824.3798319
Median Absolute Deviation (MAD)0
Skewness-1.542887266
Sum981012
Variance124285.7463
MonotonicityNot monotonic
2022-12-06T15:54:16.733952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999954
80.2%
1771
 
0.1%
1801
 
0.1%
421
 
0.1%
2021
 
0.1%
811
 
0.1%
281
 
0.1%
1611
 
0.1%
1041
 
0.1%
2201
 
0.1%
Other values (227)227
 
19.1%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
999954
80.2%
2361
 
0.1%
2351
 
0.1%
2341
 
0.1%
2331
 
0.1%
2321
 
0.1%
2311
 
0.1%
2301
 
0.1%
2291
 
0.1%
2281
 
0.1%

url
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
https://aoe-elo.com/player/767/000189AKM
 
1
https://aoe-elo.com/player/253/psNps
 
1
https://aoe-elo.com/player/1091/Project-Exile
 
1
https://aoe-elo.com/player/1268/Professor4k
 
1
https://aoe-elo.com/player/1224/prisma09
 
1
Other values (1185)
1185 

Length

Max length51
Median length47
Mean length38.51428571
Min length32

Characters and Unicode

Total characters45832
Distinct characters66
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1190 ?
Unique (%)100.0%

Sample

1st rowhttps://aoe-elo.com/player/767/000189AKM
2nd rowhttps://aoe-elo.com/player/777/000201AKM
3rd rowhttps://aoe-elo.com/player/763/000934AKM
4th rowhttps://aoe-elo.com/player/259/2scareD
5th rowhttps://aoe-elo.com/player/451/31-

Common Values

ValueCountFrequency (%)
https://aoe-elo.com/player/767/000189AKM1
 
0.1%
https://aoe-elo.com/player/253/psNps1
 
0.1%
https://aoe-elo.com/player/1091/Project-Exile1
 
0.1%
https://aoe-elo.com/player/1268/Professor4k1
 
0.1%
https://aoe-elo.com/player/1224/prisma091
 
0.1%
https://aoe-elo.com/player/1200/pren1
 
0.1%
https://aoe-elo.com/player/686/Prelude1
 
0.1%
https://aoe-elo.com/player/482/Pray-4yourlife1
 
0.1%
https://aoe-elo.com/player/126/PraYs1
 
0.1%
https://aoe-elo.com/player/338/pRaXiS1
 
0.1%
Other values (1180)1180
99.2%

Length

2022-12-06T15:54:17.110983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://aoe-elo.com/player/767/000189akm1
 
0.1%
https://aoe-elo.com/player/763/000934akm1
 
0.1%
https://aoe-elo.com/player/451/311
 
0.1%
https://aoe-elo.com/player/843/3nvy1
 
0.1%
https://aoe-elo.com/player/466/7demons1
 
0.1%
https://aoe-elo.com/player/56/8th-wonder1
 
0.1%
https://aoe-elo.com/player/1129/a2205415151
 
0.1%
https://aoe-elo.com/player/561/aaiioo1
 
0.1%
https://aoe-elo.com/player/844/aa-nike1
 
0.1%
https://aoe-elo.com/player/515/abe1
 
0.1%
Other values (1180)1180
99.2%

Most occurring characters

ValueCountFrequency (%)
/5950
 
13.0%
e4260
 
9.3%
o4136
 
9.0%
a3103
 
6.8%
l2726
 
5.9%
t2669
 
5.8%
p2496
 
5.4%
r1652
 
3.6%
-1622
 
3.5%
s1486
 
3.2%
Other values (56)15732
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter30159
65.8%
Other Punctuation8330
 
18.2%
Decimal Number3968
 
8.7%
Uppercase Letter1753
 
3.8%
Dash Punctuation1622
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4260
14.1%
o4136
13.7%
a3103
10.3%
l2726
9.0%
t2669
8.8%
p2496
8.3%
r1652
 
5.5%
s1486
 
4.9%
h1410
 
4.7%
c1365
 
4.5%
Other values (16)4856
16.1%
Uppercase Letter
ValueCountFrequency (%)
S155
 
8.8%
M121
 
6.9%
T117
 
6.7%
A107
 
6.1%
D107
 
6.1%
R105
 
6.0%
C104
 
5.9%
N90
 
5.1%
B84
 
4.8%
K76
 
4.3%
Other values (16)687
39.2%
Decimal Number
ValueCountFrequency (%)
1728
18.3%
2453
11.4%
0376
9.5%
4359
9.0%
3356
9.0%
5344
8.7%
8343
8.6%
6338
8.5%
9337
8.5%
7334
8.4%
Other Punctuation
ValueCountFrequency (%)
/5950
71.4%
:1190
 
14.3%
.1190
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
-1622
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin31912
69.6%
Common13920
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4260
13.3%
o4136
13.0%
a3103
9.7%
l2726
8.5%
t2669
8.4%
p2496
 
7.8%
r1652
 
5.2%
s1486
 
4.7%
h1410
 
4.4%
c1365
 
4.3%
Other values (42)6609
20.7%
Common
ValueCountFrequency (%)
/5950
42.7%
-1622
 
11.7%
:1190
 
8.5%
.1190
 
8.5%
1728
 
5.2%
2453
 
3.3%
0376
 
2.7%
4359
 
2.6%
3356
 
2.6%
5344
 
2.5%
Other values (4)1352
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII45832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/5950
 
13.0%
e4260
 
9.3%
o4136
 
9.0%
a3103
 
6.8%
l2726
 
5.9%
t2669
 
5.8%
p2496
 
5.4%
r1652
 
3.6%
-1622
 
3.5%
s1486
 
3.2%
Other values (56)15732
34.3%

voobly_id
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct95
Distinct (%)100.0%
Missing1095
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean96393527.33
Minimum523
Maximum125101127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:17.449034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum523
5-th percentile4117.2
Q1123160364
median123444726
Q3123924317.5
95-th percentile124543846.3
Maximum125101127
Range125100604
Interquartile range (IQR)763953.5

Descriptive statistics

Standard deviation51592839.16
Coefficient of variation (CV)0.5352313645
Kurtosis-0.1372811508
Mean96393527.33
Median Absolute Deviation (MAD)465457
Skewness-1.365888318
Sum9157385096
Variance2.661821053 × 1015
MonotonicityNot monotonic
2022-12-06T15:54:18.059814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1237379201
 
0.1%
1234979261
 
0.1%
1232251391
 
0.1%
1246533231
 
0.1%
1248272721
 
0.1%
1231741381
 
0.1%
1234174491
 
0.1%
54691
 
0.1%
565311
 
0.1%
1232227101
 
0.1%
Other values (85)85
 
7.1%
(Missing)1095
92.0%
ValueCountFrequency (%)
5231
0.1%
6101
0.1%
7221
0.1%
8171
0.1%
9631
0.1%
54691
0.1%
59581
0.1%
92671
0.1%
114861
0.1%
129261
0.1%
ValueCountFrequency (%)
1251011271
0.1%
1249548671
0.1%
1248272721
0.1%
1246533231
0.1%
1246466281
0.1%
1244997971
0.1%
1244746471
0.1%
1244347551
0.1%
1243865241
0.1%
1242449371
0.1%

steam_id
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1190
Missing (%)100.0%
Memory size9.4 KiB

first_series_timestamp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct329
Distinct (%)28.0%
Missing14
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1370285485
Minimum965260800
Maximum1665878400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:18.353875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum965260800
5-th percentile997574399
Q11082851200
median1426161600
Q31612828800
95-th percentile1649376000
Maximum1665878400
Range700617600
Interquartile range (IQR)529977600

Descriptive statistics

Standard deviation251478248.3
Coefficient of variation (CV)0.1835225221
Kurtosis-1.591971246
Mean1370285485
Median Absolute Deviation (MAD)197553600
Skewness-0.3097189565
Sum1.611455731 × 1012
Variance6.324130937 × 1016
MonotonicityNot monotonic
2022-12-06T15:54:18.709905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162285120041
 
3.4%
162224640040
 
3.4%
107576639939
 
3.3%
100759680032
 
2.7%
103645439925
 
2.1%
120026879924
 
2.0%
158716800024
 
2.0%
140832000021
 
1.8%
126230399921
 
1.8%
137617920020
 
1.7%
Other values (319)889
74.7%
ValueCountFrequency (%)
9652608002
 
0.2%
9654336002
 
0.2%
96552000010
0.8%
9656064004
 
0.3%
9656928004
 
0.3%
9657792002
 
0.2%
9659520004
 
0.3%
9667296002
 
0.2%
9716543993
 
0.3%
98375039914
1.2%
ValueCountFrequency (%)
16658784001
0.1%
16657920001
0.1%
16657056002
0.2%
16656192001
0.1%
16655328002
0.2%
16653600001
0.1%
16651872001
0.1%
16651008001
0.1%
16639776002
0.2%
16638912001
0.1%

first_series_time
Categorical

HIGH CARDINALITY
MISSING

Distinct121
Distinct (%)10.3%
Missing14
Missing (%)1.2%
Memory size9.4 KiB
Jun 2021
 
68
May 2021
 
59
Apr 2020
 
54
Feb 2004
 
39
Aug 2020
 
38
Other values (116)
918 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters9408
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)1.0%

Sample

1st rowNov 2002
2nd rowNov 2002
3rd rowNov 2002
4th rowApr 2003
5th rowMar 2010

Common Values

ValueCountFrequency (%)
Jun 202168
 
5.7%
May 202159
 
5.0%
Apr 202054
 
4.5%
Feb 200439
 
3.3%
Aug 202038
 
3.2%
Dec 200132
 
2.7%
Aug 200030
 
2.5%
Nov 200225
 
2.1%
Oct 202125
 
2.1%
Jan 200824
 
2.0%
Other values (111)782
65.7%

Length

2022-12-06T15:54:19.006925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021238
 
10.1%
2020168
 
7.1%
aug161
 
6.8%
apr141
 
6.0%
jan129
 
5.5%
dec117
 
5.0%
jun116
 
4.9%
may106
 
4.5%
oct91
 
3.9%
202286
 
3.7%
Other values (25)999
42.5%

Most occurring characters

ValueCountFrequency (%)
21847
19.6%
01846
19.6%
1176
12.5%
1549
 
5.8%
u362
 
3.8%
J330
 
3.5%
a303
 
3.2%
A302
 
3.2%
n245
 
2.6%
e215
 
2.3%
Other values (23)2233
23.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4704
50.0%
Lowercase Letter2352
25.0%
Space Separator1176
 
12.5%
Uppercase Letter1176
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u362
15.4%
a303
12.9%
n245
10.4%
e215
9.1%
r209
8.9%
c208
8.8%
p167
7.1%
g161
6.8%
y106
 
4.5%
t91
 
3.9%
Other values (4)285
12.1%
Decimal Number
ValueCountFrequency (%)
21847
39.3%
01846
39.2%
1549
 
11.7%
4116
 
2.5%
3105
 
2.2%
572
 
1.5%
750
 
1.1%
848
 
1.0%
945
 
1.0%
626
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
J330
28.1%
A302
25.7%
M174
14.8%
D117
 
9.9%
O91
 
7.7%
F72
 
6.1%
N64
 
5.4%
S26
 
2.2%
Space Separator
ValueCountFrequency (%)
1176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5880
62.5%
Latin3528
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u362
 
10.3%
J330
 
9.4%
a303
 
8.6%
A302
 
8.6%
n245
 
6.9%
e215
 
6.1%
r209
 
5.9%
c208
 
5.9%
M174
 
4.9%
p167
 
4.7%
Other values (12)1013
28.7%
Common
ValueCountFrequency (%)
21847
31.4%
01846
31.4%
1176
20.0%
1549
 
9.3%
4116
 
2.0%
3105
 
1.8%
572
 
1.2%
750
 
0.9%
848
 
0.8%
945
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII9408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21847
19.6%
01846
19.6%
1176
12.5%
1549
 
5.8%
u362
 
3.8%
J330
 
3.5%
a303
 
3.2%
A302
 
3.2%
n245
 
2.6%
e215
 
2.3%
Other values (23)2233
23.7%

peak_timestamp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct414
Distinct (%)35.2%
Missing14
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1392408367
Minimum965260800
Maximum1666483200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:19.283485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum965260800
5-th percentile1007596800
Q11105747200
median1500681600
Q31622851200
95-th percentile1660154400
Maximum1666483200
Range701222400
Interquartile range (IQR)517104000

Descriptive statistics

Standard deviation255260806.5
Coefficient of variation (CV)0.1833232351
Kurtosis-1.564211531
Mean1392408367
Median Absolute Deviation (MAD)149904000
Skewness-0.4015797184
Sum1.63747224 × 1012
Variance6.515807931 × 1016
MonotonicityNot monotonic
2022-12-06T15:54:19.629514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162285120044
 
3.7%
107576639942
 
3.5%
162224640033
 
2.8%
103645439930
 
2.5%
100759680028
 
2.4%
163555200023
 
1.9%
120026879921
 
1.8%
126230399918
 
1.5%
158716800018
 
1.5%
159744960017
 
1.4%
Other values (404)902
75.8%
ValueCountFrequency (%)
9652608001
 
0.1%
9654336002
 
0.2%
9655200007
0.6%
9656064002
 
0.2%
9656928002
 
0.2%
9657792001
 
0.1%
9659520003
0.3%
9664704001
 
0.1%
9667296002
 
0.2%
9716543993
0.3%
ValueCountFrequency (%)
16664832002
 
0.2%
16663104002
 
0.2%
16662240001
 
0.1%
16658784003
0.3%
16657920005
0.4%
16657056003
0.3%
16656192001
 
0.1%
16655328003
0.3%
16653600002
 
0.2%
16651872001
 
0.1%

peak_time
Categorical

HIGH CARDINALITY
MISSING

Distinct130
Distinct (%)11.1%
Missing14
Missing (%)1.2%
Memory size9.4 KiB
Jun 2021
 
68
May 2021
 
49
Feb 2004
 
42
Apr 2020
 
41
Aug 2020
 
40
Other values (125)
936 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters9408
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)1.9%

Sample

1st rowNov 2002
2nd rowNov 2002
3rd rowNov 2002
4th rowApr 2003
5th rowMar 2010

Common Values

ValueCountFrequency (%)
Jun 202168
 
5.7%
May 202149
 
4.1%
Feb 200442
 
3.5%
Apr 202041
 
3.4%
Aug 202040
 
3.4%
Oct 202134
 
2.9%
Apr 202231
 
2.6%
Nov 200230
 
2.5%
Dec 200129
 
2.4%
Oct 202227
 
2.3%
Other values (120)785
66.0%

Length

2022-12-06T15:54:19.954536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021249
 
10.6%
2022167
 
7.1%
aug163
 
6.9%
2020145
 
6.2%
apr134
 
5.7%
jan123
 
5.2%
jun112
 
4.8%
oct108
 
4.6%
dec105
 
4.5%
may97
 
4.1%
Other values (25)949
40.3%

Most occurring characters

ValueCountFrequency (%)
21989
21.1%
01794
19.1%
1176
12.5%
1501
 
5.3%
u364
 
3.9%
J324
 
3.4%
A297
 
3.2%
a282
 
3.0%
n235
 
2.5%
e224
 
2.4%
Other values (23)2222
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4704
50.0%
Lowercase Letter2352
25.0%
Space Separator1176
 
12.5%
Uppercase Letter1176
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u364
15.5%
a282
12.0%
n235
10.0%
e224
9.5%
c213
9.1%
r196
8.3%
p172
7.3%
g163
6.9%
t108
 
4.6%
y97
 
4.1%
Other values (4)298
12.7%
Decimal Number
ValueCountFrequency (%)
21989
42.3%
01794
38.1%
1501
 
10.7%
4113
 
2.4%
383
 
1.8%
576
 
1.6%
847
 
1.0%
741
 
0.9%
934
 
0.7%
626
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
J324
27.6%
A297
25.3%
M159
13.5%
O108
 
9.2%
D105
 
8.9%
F81
 
6.9%
N64
 
5.4%
S38
 
3.2%
Space Separator
ValueCountFrequency (%)
1176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5880
62.5%
Latin3528
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u364
 
10.3%
J324
 
9.2%
A297
 
8.4%
a282
 
8.0%
n235
 
6.7%
e224
 
6.3%
c213
 
6.0%
r196
 
5.6%
p172
 
4.9%
g163
 
4.6%
Other values (12)1058
30.0%
Common
ValueCountFrequency (%)
21989
33.8%
01794
30.5%
1176
20.0%
1501
 
8.5%
4113
 
1.9%
383
 
1.4%
576
 
1.3%
847
 
0.8%
741
 
0.7%
934
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII9408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21989
21.1%
01794
19.1%
1176
12.5%
1501
 
5.3%
u364
 
3.9%
J324
 
3.4%
A297
 
3.2%
a282
 
3.0%
n235
 
2.5%
e224
 
2.4%
Other values (23)2222
23.6%

peak_elo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct168
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1942.153782
Minimum1880
Maximum2395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:20.256586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1880
5-th percentile1892
Q11896
median1900.5
Q31994.75
95-th percentile2071.55
Maximum2395
Range515
Interquartile range (IQR)98.75

Descriptive statistics

Standard deviation78.12187142
Coefficient of variation (CV)0.04022434895
Kurtosis7.20981157
Mean1942.153782
Median Absolute Deviation (MAD)6.5
Skewness2.378947128
Sum2311163
Variance6103.026794
MonotonicityNot monotonic
2022-12-06T15:54:20.619640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1896181
 
15.2%
1894108
 
9.1%
1897100
 
8.4%
189858
 
4.9%
190454
 
4.5%
189144
 
3.7%
190035
 
2.9%
189926
 
2.2%
189226
 
2.2%
199622
 
1.8%
Other values (158)536
45.0%
ValueCountFrequency (%)
18801
 
0.1%
18852
 
0.2%
188810
 
0.8%
18901
 
0.1%
189144
 
3.7%
189226
 
2.2%
18932
 
0.2%
1894108
9.1%
18951
 
0.1%
1896181
15.2%
ValueCountFrequency (%)
23951
0.1%
23841
0.1%
23711
0.1%
23571
0.1%
23491
0.1%
23401
0.1%
23331
0.1%
23061
0.1%
23051
0.1%
22961
0.1%

inactive
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
871 
False
319 
ValueCountFrequency (%)
True871
73.2%
False319
 
26.8%
2022-12-06T15:54:20.911646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

retired
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
657 
False
533 
ValueCountFrequency (%)
True657
55.2%
False533
44.8%
2022-12-06T15:54:21.156665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

series_played
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct108
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.88571429
Minimum0
Maximum486
Zeros14
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:21.418722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q39
95-th percentile66
Maximum486
Range486
Interquartile range (IQR)8

Descriptive statistics

Standard deviation41.54103079
Coefficient of variation (CV)2.790664256
Kurtosis43.45872282
Mean14.88571429
Median Absolute Deviation (MAD)2
Skewness5.931018445
Sum17714
Variance1725.657239
MonotonicityNot monotonic
2022-12-06T15:54:21.754748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1403
33.9%
2141
 
11.8%
3103
 
8.7%
569
 
5.8%
465
 
5.5%
830
 
2.5%
628
 
2.4%
1024
 
2.0%
922
 
1.8%
722
 
1.8%
Other values (98)283
23.8%
ValueCountFrequency (%)
014
 
1.2%
1403
33.9%
2141
 
11.8%
3103
 
8.7%
465
 
5.5%
569
 
5.8%
628
 
2.4%
722
 
1.8%
830
 
2.5%
922
 
1.8%
ValueCountFrequency (%)
4861
0.1%
3911
0.1%
3872
0.2%
3511
0.1%
3181
0.1%
2771
0.1%
2701
0.1%
2491
0.1%
2481
0.1%
2351
0.1%

series_won
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct78
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.217647059
Minimum0
Maximum331
Zeros594
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:22.106775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile32
Maximum331
Range331
Interquartile range (IQR)3

Descriptive statistics

Standard deviation26.22399195
Coefficient of variation (CV)3.633315918
Kurtosis62.93328525
Mean7.217647059
Median Absolute Deviation (MAD)1
Skewness7.104767386
Sum8589
Variance687.6977539
MonotonicityNot monotonic
2022-12-06T15:54:22.436800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0594
49.9%
1185
 
15.5%
287
 
7.3%
352
 
4.4%
435
 
2.9%
518
 
1.5%
718
 
1.5%
816
 
1.3%
615
 
1.3%
1413
 
1.1%
Other values (68)157
 
13.2%
ValueCountFrequency (%)
0594
49.9%
1185
 
15.5%
287
 
7.3%
352
 
4.4%
435
 
2.9%
518
 
1.5%
615
 
1.3%
718
 
1.5%
816
 
1.3%
910
 
0.8%
ValueCountFrequency (%)
3311
0.1%
3241
0.1%
2651
0.1%
2491
0.1%
2061
0.1%
2041
0.1%
1721
0.1%
1611
0.1%
1601
0.1%
1521
0.1%

games_played
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct183
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.51092437
Minimum0
Maximum1644
Zeros19
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:23.092891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q323
95-th percentile196.75
Maximum1644
Range1644
Interquartile range (IQR)21

Descriptive statistics

Standard deviation136.7270902
Coefficient of variation (CV)3.142362341
Kurtosis48.66558153
Mean43.51092437
Median Absolute Deviation (MAD)4
Skewness6.286789386
Sum51778
Variance18694.29719
MonotonicityNot monotonic
2022-12-06T15:54:23.387934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2215
18.1%
1125
 
10.5%
395
 
8.0%
478
 
6.6%
570
 
5.9%
642
 
3.5%
731
 
2.6%
828
 
2.4%
1321
 
1.8%
1220
 
1.7%
Other values (173)465
39.1%
ValueCountFrequency (%)
019
 
1.6%
1125
10.5%
2215
18.1%
395
8.0%
478
 
6.6%
570
 
5.9%
642
 
3.5%
731
 
2.6%
828
 
2.4%
919
 
1.6%
ValueCountFrequency (%)
16441
0.1%
13641
0.1%
13631
0.1%
12281
0.1%
11801
0.1%
10591
0.1%
9191
0.1%
9011
0.1%
8151
0.1%
7821
0.1%

tournaments_played
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct69
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.103361345
Minimum0
Maximum127
Zeros14
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2022-12-06T15:54:23.713959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q35
95-th percentile30
Maximum127
Range127
Interquartile range (IQR)4

Descriptive statistics

Standard deviation13.43139083
Coefficient of variation (CV)2.200654699
Kurtosis25.38008336
Mean6.103361345
Median Absolute Deviation (MAD)0
Skewness4.587029648
Sum7263
Variance180.4022595
MonotonicityNot monotonic
2022-12-06T15:54:24.071989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1620
52.1%
2137
 
11.5%
362
 
5.2%
457
 
4.8%
542
 
3.5%
634
 
2.9%
724
 
2.0%
1016
 
1.3%
816
 
1.3%
915
 
1.3%
Other values (59)167
 
14.0%
ValueCountFrequency (%)
014
 
1.2%
1620
52.1%
2137
 
11.5%
362
 
5.2%
457
 
4.8%
542
 
3.5%
634
 
2.9%
724
 
2.0%
816
 
1.3%
915
 
1.3%
ValueCountFrequency (%)
1271
0.1%
1131
0.1%
1111
0.1%
1061
0.1%
1011
0.1%
891
0.1%
881
0.1%
861
0.1%
831
0.1%
821
0.1%

tournaments_list
Categorical

HIGH CARDINALITY

Distinct697
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
[293]
 
31
[127]
 
27
[142]
 
25
[290]
 
19
[136]
 
19
Other values (692)
1069 

Length

Max length588
Median length533
Mean length29.46470588
Min length2

Characters and Unicode

Total characters35063
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique588 ?
Unique (%)49.4%

Sample

1st row[136]
2nd row[136]
3rd row[136]
4th row[86, 110, 114, 115, 116, 117, 119, 121, 123, 124, 125, 126, 127, 157, 264, 285]
5th row[86]

Common Values

ValueCountFrequency (%)
[293]31
 
2.6%
[127]27
 
2.3%
[142]25
 
2.1%
[290]19
 
1.6%
[136]19
 
1.6%
[148]18
 
1.5%
[102]16
 
1.3%
[343]15
 
1.3%
[]14
 
1.2%
[264]14
 
1.2%
Other values (687)992
83.4%

Length

2022-12-06T15:54:24.416560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
293128
 
1.8%
290128
 
1.8%
343126
 
1.7%
31596
 
1.3%
29191
 
1.3%
30479
 
1.1%
29878
 
1.1%
26270
 
1.0%
12765
 
0.9%
35364
 
0.9%
Other values (462)6352
87.3%

Most occurring characters

ValueCountFrequency (%)
,6087
17.4%
6087
17.4%
33565
10.2%
13215
9.2%
23025
8.6%
42334
 
6.7%
91660
 
4.7%
61470
 
4.2%
01421
 
4.1%
71298
 
3.7%
Other values (4)4901
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20509
58.5%
Other Punctuation6087
 
17.4%
Space Separator6087
 
17.4%
Open Punctuation1190
 
3.4%
Close Punctuation1190
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
33565
17.4%
13215
15.7%
23025
14.7%
42334
11.4%
91660
8.1%
61470
7.2%
01421
 
6.9%
71298
 
6.3%
51277
 
6.2%
81244
 
6.1%
Other Punctuation
ValueCountFrequency (%)
,6087
100.0%
Space Separator
ValueCountFrequency (%)
6087
100.0%
Open Punctuation
ValueCountFrequency (%)
[1190
100.0%
Close Punctuation
ValueCountFrequency (%)
]1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35063
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,6087
17.4%
6087
17.4%
33565
10.2%
13215
9.2%
23025
8.6%
42334
 
6.7%
91660
 
4.7%
61470
 
4.2%
01421
 
4.1%
71298
 
3.7%
Other values (4)4901
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35063
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,6087
17.4%
6087
17.4%
33565
10.2%
13215
9.2%
23025
8.6%
42334
 
6.7%
91660
 
4.7%
61470
 
4.2%
01421
 
4.1%
71298
 
3.7%
Other values (4)4901
14.0%

Interactions

2022-12-06T15:54:07.313537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:23.705374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:27.607883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:31.414796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:34.983430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:38.781944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:43.317448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:47.043992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:51.149698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:55.317055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:59.377470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:02.816481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:07.546554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:24.018898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:27.892454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:31.742936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:35.264452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:39.124016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:43.652468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:47.340458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:51.520726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:55.640119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:59.678490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:03.135506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:07.846106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:24.310954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:28.242149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:32.055957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:35.556001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:39.406036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:43.938488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:47.674023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:51.866758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:55.960141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:59.960534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:03.468167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:08.117128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:24.612005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:28.577180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:32.367544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:35.846026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:39.687056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:44.283533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:48.006048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:52.151774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:56.242159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:00.268556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:03.765718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:08.353188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:24.961036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:28.896203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:32.666566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:36.115047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:39.953118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:44.540554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:48.313203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:52.471797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:56.490916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:00.538577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:04.371774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:08.501198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:25.258092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:29.152217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:32.898592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:36.352060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:40.179133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:44.835579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:48.610250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:52.816826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:57.110966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:00.763597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:04.683800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:08.856112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:25.599861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:29.440741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:33.212354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:36.710216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:40.466159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:45.151169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:49.269831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:53.265860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:57.403551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:01.071855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:05.082828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:09.077132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:25.921455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:29.768299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:33.514776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:37.082243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:40.756180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:45.518901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:49.571849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:53.682889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:57.781580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:01.349877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:05.533863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:09.392187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:26.328229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:30.068321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:33.795681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:37.437724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:42.193816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:45.801920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:49.881925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:54.053918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:58.111608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:01.590890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:05.990897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:09.742755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:26.651788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:30.331083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:34.083701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:37.762753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:42.485837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:46.107826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:50.154374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:54.389944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:58.416627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:01.864000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:06.369927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:10.089786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:26.984810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:30.715383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:34.367276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:38.097800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:42.753858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:46.411865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:50.454412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:54.717967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:58.705648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:02.181020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:06.665948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:10.439806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:27.216853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:31.037765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:34.677297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:38.453385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:43.031914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:46.732962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:50.846662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:55.000987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:53:59.003438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:02.506459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-06T15:54:07.002975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-12-06T15:54:24.711450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-12-06T15:54:25.182474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-06T15:54:25.763093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-06T15:54:26.264132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-06T15:54:26.777727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-06T15:54:27.095752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-06T15:54:10.929843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-06T15:54:12.296225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-06T15:54:12.856270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-12-06T15:54:13.276301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0idnameelorankurlvoobly_idsteam_idfirst_series_timestampfirst_series_timepeak_timestamppeak_timepeak_eloinactiveretiredseries_playedseries_wongames_playedtournaments_playedtournaments_list
00767000189AKM1894999https://aoe-elo.com/player/767/000189AKMNaNNaN1.036454e+09Nov 20021.036454e+09Nov 20021896TrueTrue4141[136]
11777000201AKM1886999https://aoe-elo.com/player/777/000201AKMNaNNaN1.036454e+09Nov 20021.036454e+09Nov 20021897TrueTrue4041[136]
22763000934AKM1882999https://aoe-elo.com/player/763/000934AKMNaNNaN1.036454e+09Nov 20021.036454e+09Nov 20021896TrueTrue5051[136]
332592scareD1957999https://aoe-elo.com/player/259/2scareDNaNNaN1.049501e+09Apr 20031.049501e+09Apr 20031996TrueTrue31118416[86, 110, 114, 115, 116, 117, 119, 121, 123, 124, 125, 126, 127, 157, 264, 285]
4445131_1886999https://aoe-elo.com/player/451/31-NaNNaN1.267834e+09Mar 20101.267834e+09Mar 20101893TrueTrue20101[86]
558433nvY1897999https://aoe-elo.com/player/843/3nvYNaNNaN1.039565e+09Dec 20021.039565e+09Dec 20021897TrueTrue1011[137]
664667DeMonS1902999https://aoe-elo.com/player/466/7DeMonSNaNNaN1.075766e+09Feb 20041.075766e+09Feb 20041904TrueTrue3172[126, 127]
77568th_wonder1991999https://aoe-elo.com/player/56/8th-wonder123737920.0NaN1.307923e+09Jun 20111.442707e+09Sep 20152039TrueTrue301110110[15, 19, 21, 29, 62, 65, 80, 84, 85, 103]
881129a2205415151900999https://aoe-elo.com/player/1129/a220541515NaNNaNNaNNaNNaNNaN1900FalseFalse0000[]
99561aaiioo1900999https://aoe-elo.com/player/561/aaiiooNaNNaNNaNNaNNaNNaN1900FalseFalse0000[]

Last rows

Unnamed: 0idnameelorankurlvoobly_idsteam_idfirst_series_timestampfirst_series_timepeak_timestamppeak_timepeak_eloinactiveretiredseries_playedseries_wongames_playedtournaments_playedtournaments_list
11801180862_Tulkas1894999https://aoe-elo.com/player/862/-TulkasNaNNaN1.409875e+09Sep 20141.409875e+09Sep 20141894TrueTrue1021[75]
11811181714_ViRuS_PuLsE1966150https://aoe-elo.com/player/714/-ViRuS-PuLsENaNNaN1.589933e+09May 20201.589933e+09May 20201988FalseFalse82292[173, 368]
11821182412_ZhaZ1995999https://aoe-elo.com/player/412/-ZhaZNaNNaN1.005178e+09Nov 20011.012522e+09Jan 20022007TrueTrue126124[141, 142, 143, 146]
11831183678__BillaBamZilla1900999https://aoe-elo.com/player/678/--BillaBamZillaNaNNaN1.082851e+09Apr 20041.082851e+09Apr 20041904TrueTrue2121[125]
118411841209__I_s_I__1899999https://aoe-elo.com/player/1209/--I-s-I--NaNNaN1.647994e+09Mar 20221.647994e+09Mar 20221912FalseFalse31101[369]
118511851193偶像先生1899999https://aoe-elo.com/player/1193/------------NaNNaN1.635552e+09Oct 20211.635552e+09Oct 20211899TrueFalse1031[343]
11861186810小布周1888999https://aoe-elo.com/player/810/----------NaNNaN1.596758e+09Aug 20201.596758e+09Aug 20201894TrueTrue2042[213, 214]
118711871146小菜鸡1881999https://aoe-elo.com/player/1146/---------NaNNaN1.627776e+09Aug 20211.627776e+09Aug 20211896TrueFalse5051[316]
118811881130細菌人1900999https://aoe-elo.com/player/1130/---------NaNNaNNaNNaNNaNNaN1900FalseFalse0000[]
118911891131龍潭金城武1878999https://aoe-elo.com/player/1131/---------------NaNNaN1.626394e+09Jul 20211.626394e+09Jul 20211894TrueFalse5092[311, 343]